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<p>Aquifer vulnerability assessment (AVA) is a critical tool for the protection of groundwater-dependent ecosystems, especially in regions under persistent anthropogenic stress. Yet conventional index-based methods often rely on static parameters that inadequately represent dynamic surface–groundwater interactions (SGWI) and evolving land use pressures. This study presents an enhanced AVA framework that integrates physically based SGWI simulations from a coupled SWAT–MODFLOW model with modified DRASTIC indices (DRASTIC, DRASTICL, and DRASTICLW), normalized using a spatially resolved Water Quality Index (WQI). The WQI served as an empirical proxy for contamination, strengthening the link between predicted vulnerability and observed groundwater quality. Key hydrogeological parameters are refined using calibrated SWAT–MODFLOW outputs to improve the representation of recharge, land-surface dynamics, and subsurface responses. The results show that moderate- to high-vulnerability zones dominate the catchment, particularly in cultivated lands and shallow aquifers. Areas with very high vulnerability, though limited, coincide with major water resources and industrial zones, indicating a practical need for targeted management interventions. Model evaluation using ROC analysis indicates improved discrimination performance from DRASTIC to DRASTICLW formulations. The integrated framework provides a physically consistent and empirically constrained approach for regional-scale vulnerability assessment, with applicability dependent on hydrogeological conditions and data availability. Comparative evaluations highlight notable shifts in vulnerability patterns when land use and water quality variables are included, confirming the influence of anthropogenic stressors. By integrating physically based hydrological modeling with empirical water quality data, this approach provides a transferable decision-support framework. It contributes to Sustainable Development Goal 6 (SDG 6) and supports adaptive groundwater management in environmentally sensitive regions.</p>

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Enhancing groundwater vulnerability assessment framework under anthropogenic stressors

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2026年5月11日

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  • Tarekegn Dejen Mengistu,
  • Il-Moon Chung,
  • Sun Woo Chang

摘要

Aquifer vulnerability assessment (AVA) is a critical tool for the protection of groundwater-dependent ecosystems, especially in regions under persistent anthropogenic stress. Yet conventional index-based methods often rely on static parameters that inadequately represent dynamic surface–groundwater interactions (SGWI) and evolving land use pressures. This study presents an enhanced AVA framework that integrates physically based SGWI simulations from a coupled SWAT–MODFLOW model with modified DRASTIC indices (DRASTIC, DRASTICL, and DRASTICLW), normalized using a spatially resolved Water Quality Index (WQI). The WQI served as an empirical proxy for contamination, strengthening the link between predicted vulnerability and observed groundwater quality. Key hydrogeological parameters are refined using calibrated SWAT–MODFLOW outputs to improve the representation of recharge, land-surface dynamics, and subsurface responses. The results show that moderate- to high-vulnerability zones dominate the catchment, particularly in cultivated lands and shallow aquifers. Areas with very high vulnerability, though limited, coincide with major water resources and industrial zones, indicating a practical need for targeted management interventions. Model evaluation using ROC analysis indicates improved discrimination performance from DRASTIC to DRASTICLW formulations. The integrated framework provides a physically consistent and empirically constrained approach for regional-scale vulnerability assessment, with applicability dependent on hydrogeological conditions and data availability. Comparative evaluations highlight notable shifts in vulnerability patterns when land use and water quality variables are included, confirming the influence of anthropogenic stressors. By integrating physically based hydrological modeling with empirical water quality data, this approach provides a transferable decision-support framework. It contributes to Sustainable Development Goal 6 (SDG 6) and supports adaptive groundwater management in environmentally sensitive regions.